Learning complex relations for session-based recommendations
Shoujin Wang · UTS ePRESS (University of Technology Sydney) · 2019
In the era of big data, recommender systems (RSs) are a powerful engine to promote intelligent life by helping humans to make decisions concerning their daily necessities (e.g., food, clothes, and houses) much more efficiently and effectively, selecting from a large number of choices.Of the various types of recommender systems, session-based (SB) ones are of great value and significance, but they are not well studied.The value of session-based recommender systems comes from two fold.From the research perspective, a SBRS takes a session as the basic unit for data organization and thus keeps the intrinsic nature of the original transaction-like data.As a result, the system effectively retains and models the rich information (e.g., intra-session dependency) embedded in a session structure to produce a more reliable recommendation.This modelling cannot be achieved by other types of recommender systems because they usually break down the original session data into multiple pair-wised user-item interactions to fit the models.From the business perspective, session data for session-based recommender systems is much more readily available than either the rating data or the item attribute data required by other recommender systems including content-based or collaborative filtering ones.This actually makes session-based RSs much more applicable in real-world business.Though valuable, SBRSs are quite challenging.Generally, a hierarchical architecture consisting of five levels (cf. Figure 1.1) is built from the low-level feature values till to the high-level sessions in session data,as demonstrated xix ABSTRACT in Chapter 1.The challenge arises mainly comes from three considerations: the heterogeneity of the elements in each level (e.g., there are both categorical and numerical features), the complex dependency within each level (e.g., ABSTRACT session-item interactions are modelled.A hierarchical attentive transaction embedding model is built to jointly model the intra-session (item-level) and inter-session (session-level) dependency.Accordingly, the influence from previous sessions on a current session is incorporated for more accurate next-item recommendations.All these models are applied to real-world transaction data, like Tmall and Tafang and they clearly outperform other representative SBRSs.More importantly, this thesis proposes a systematic framework to explore the driving force behind SBRSs, which provides some insights into both the researchers and engineers in this domain.